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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (7): 34-45    DOI: 10.11925/infotech.2096-3467.2018.0075
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Evolution and Regional Differences of E-commerce Policies for Rural Poverty Reduction Based on Topic over Time Model
Chuanming Yu1(),Yajing Guo1,Yutian Gong1,Manyu Huang2,Hufeng Peng1
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China
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[Objective] This paper reveals the evolution and regional differences of E-commerce policies for rural poverty reduction from 2008 to 2017. [Methods] First, we used the ToT (Topic over Time) model to investigate the probability distributions of time-topics and topics-words related to E-commerce policies for rural poverty reduction. Then, we analyzed the evolution of the policy contents by calculating the average intensity of topics in each year and extracted the top n topic words with the highest probabilities. Third, we divided the data from each province into the eastern, central and western regions, and then analyzed the regional differences of policies according to the probability distribution of topics and words. [Results] E-commerce policies for rural poverty reduction had the starting, exploring and developing stages. The eastern, central and western regions have different focuses on logistics, platforms and personnel training. [Limitations] The regional differences of E-commerce policies need more fine-grained analysis. [Conclusions] Compared with the traditional word frequency counting method, the ToT model effectively reveals the policy evolution and their regional differences.

Key wordsTopic over Time Model      E-commerce Policy for Rural Poverty Reduction      Regional Difference Analysis      Policy Evolution     
Received: 22 January 2018      Published: 15 August 2018

Cite this article:

Chuanming Yu,Yajing Guo,Yutian Gong,Manyu Huang,Hufeng Peng. Evolution and Regional Differences of E-commerce Policies for Rural Poverty Reduction Based on Topic over Time Model. Data Analysis and Knowledge Discovery, 2018, 2(7): 34-45.

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